Dynamic Borrowing Research Articles

Pharmacometrics-Enhanced Bayesian Borrowing for Pediatric Extrapolation - A Case Study of the DINAMO Trial.

Bayesian borrowing analyses have an important role in the design and analysis of pediatric trials. This paper describes use of a prespecified Pharmacometrics Enhanced Bayesian Borrowing (PEBB) analysis that was conducted to overcome an expectation for reduced statistical power in the pediatric DINAMO trial due to a greater than expected variability in the primary endpoint. The DINAMO trial assessed the efficacy and safety of an empagliflozin dosing regimen versus placebo and linagliptin versus placebo on glycemic control (change in HbA1c over 26 weeks) in young people with type 2 diabetes (T2D). Previously fitted pharmacokinetic and exposure-response models for empagliflozin and linagliptin based on available historical data in adult and pediatric patients with T2D were used to simulate participant data and derive the informative component of a Bayesian robust mixture prior distribution. External experts and representatives from the U.S. Food and Drug Administration provided recommendations to determine the effective sample size of the prior and the weight of the informative prior component. Separate exposure response-based Bayesian borrowing analyses for empagliflozin and linagliptin showed posterior mean and 95% credible intervals that were consistent with the trial results. Sensitivity analyses with a full range of alternative weights were also performed. The use of PEBB in this analysis combined advantages of mechanistic modeling of pharmacometric differences between adults and young people with T2D, with advantages of partial extrapolation through Bayesian dynamic borrowing. Our findings suggest that the described PEBB approach is a promising option to optimize the power for future pediatric trials.

Leveraging information from secondary endpoints to enhance dynamic borrowing across subpopulations.

Randomized trials seek efficient treatment effect estimation within target populations, yet scientific interest often also centers on subpopulations. Although there are typically too few subjects within each subpopulation to efficiently estimate these subpopulation treatment effects, one can gain precision by borrowing strength across subpopulations, as is the case in a basket trial. While dynamic borrowing has been proposed as an efficient approach to estimating subpopulation treatment effects on primary endpoints, additional efficiency could be gained by leveraging the information found in secondary endpoints. We propose a multisource exchangeability model (MEM) that incorporates secondary endpoints to more efficiently assess subpopulation exchangeability. Across simulation studies, our proposed model almost uniformly reduces the mean squared error when compared to the standard MEM that only considers data from the primary endpoint by gaining efficiency when subpopulations respond similarly to the treatment and reducing the magnitude of bias when the subpopulations are heterogeneous. We illustrate our model's feasibility using data from a recently completed trial of very low nicotine content cigarettes to estimate the effect on abstinence from smoking within three priority subpopulations. Our proposed model led to increases in the effective sample size two to four times greater than under the standard MEM.

Dynamic borrowing of historical controls adjusting for covariates in vaccine efficacy clinical trials.

Traditional vaccine efficacy trials usually use fixed designs and often require large sample sizes. Recruiting a large number of subjects can make the trial expensive, long, and difficult to conduct. A possible approach to reduce the sample size and speed up the development is to use historical controls. In this paper, we extend the robust mixture prior (RMP) approach (a well established approach for Bayesian dynamic borrowing of historical controls) to adjust for covariates. The adjustment is done using classical methods from causal inference: inverse probability of treatment weighting, G-computation and double-robust estimation. We evaluate these covariate-adjusted RMP approaches using a comprehensive simulation study and demonstrate their use by performing a retrospective analysis of a prophylactic human papillomavirus vaccine efficacy trial. Adjusting for covariates reduces the drift between current and historical controls, with a beneficial effect on bias, control of type I error and power.

405 Bayesian Dynamic Borrowing Approach for APPARENT Phase 3 Study of Iptacopan in IC-MPGN

405 Bayesian Dynamic Borrowing Approach for APPARENT Phase 3 Study of Iptacopan in IC-MPGN

Abstract 2389: Empowering early drug development in neoadjuvant non-small cell lung cancer studies using an innovative proof-of-concept platform trial design

Abstract Several platform trial designs have recently been proposed in oncology. However, developing an efficient one in the neoadjuvant non-small cell lung cancer (NSCLC) is not easy due to the difficulties in patient recruitment and limited relevant data. These obstacles hinder the inclusion of a control arm, thereby losing the ability to make an informed Go/No-Go decision, and to assess prognostic and predictive biomarkers. To overcome these challenges, we have developed an innovative and efficient proof-of-concept platform trial design that aims to empower drug development in early stage of neoadjuvant NSCLC. The proposed platform trial incorporates available experimental agents, allowing for new agents to be added when available. Within the master protocol structure, a small control arm is included, only about 20% of experimental arm size, and is shared by all experimental arms for the analysis purpose. Consequently, the resulting direct comparison with the control arm enables an informed Go/No-Go decision-making, while the total sample size and budget impacts are minimized. The flexibility of Go/No-Go decision-making is further enhanced in a 3-outcome decision-making framework where a consider zone is introduced for physicians to examine the totality of data. Two innovative statistical techniques are incorporated: Bayesian dynamic borrowing for current control, and response-adaptive randomization (RAR). The former helps reduce the sample size, and the latter aims to optimize patient allocation and improve the ability to identify promising agents. Simulation results demonstrate that the direct comparison sharply reduces the false positive rate below 10%, compared to a single-arm design setting. The effective sample size of the control arm is statistically augmented through Bayesian dynamic borrowing. The patient allocation is guided based on the agent’s efficacy data with a well-calibrated RAR. The promising agent receives meaningfully more patients than if a fixed-randomization is used, while the variability in patient allocation is decreased. Compared to a traditional design, our innovative design boosts the probability of identifying promising agent by up to 2-fold. To conclude, this research develops an innovative platform trial design to improve the success of drug development in early stage NSCLC. The master protocol structure, inclusion of a small control arm, and dynamic borrowing, increase the plausibility of conducting a trial in the neoadjuvant population. Advanced statistical features optimize the patient allocation and enhance the ability to identify promising drugs. Lastly, the proposed design can be easily applied to other oncology indications with appropriate modifications. Citation Format: Zhe Chen, Phillip A. Dennis, Ji Lin. Empowering early drug development in neoadjuvant non-small cell lung cancer studies using an innovative proof-of-concept platform trial design [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2389.

Digital twins and Bayesian dynamic borrowing: Two recent approaches for incorporating historical control data.

Recent years have seen an increasing interest in incorporating external control data for designing and evaluating randomized clinical trials (RCT). This may decrease costs and shorten inclusion times by reducing sample sizes. For small populations, with limited recruitment, this can be especially important. Bayesian dynamic borrowing (BDB) has been a popular choice as it claims to protect against potential prior data conflict. Digital twins (DT) has recently been proposed as another method to utilize historical data. DT, also known as PROCOVA™, is based on constructing a prognostic score from historical control data, typically using machine learning. This score is included in a pre-specified ANCOVA as the primary analysis of the RCT. The promise of this idea is power increase while guaranteeing strong type 1 error control. In this paper, we apply analytic derivations and simulations to analyze and discuss examples of these two approaches. We conclude that BDB and DT, although similar in scope, have fundamental differences which need be considered in the specific application. The inflation of the type 1 error is a serious issue for BDB, while more evidence is needed of a tangible value of DT for real RCTs.

A review of dynamic borrowing methods with applications in pharmaceutical research

A review of dynamic borrowing methods with applications in pharmaceutical research

A scaled kernel density estimation prior for dynamic borrowing of historical information with application to clinical trial design.

Incorporating historical data into a current data analysis can improve estimation of parameters shared across both datasets and increase the power to detect associations of interest while reducing the time and cost of new data collection. Several methods for prior distribution elicitation have been introduced to allow for the data-driven borrowing of historical information within a Bayesian analysis of the current data. We propose scaled Gaussian kernel density estimation (SGKDE) prior distributions as potentially more flexible alternatives. SGKDE priors directly use posterior samples collected from a historical data analysis to approximate probability density functions, whose variances depend on the degree of similarity between the historical and current datasets, which are used as prior distributions in the current data analysis. We compare the performances of the SGKDE priors with some existing approaches using a simulation study. Data from a recently completed phase III clinical trial of a maternal vaccine for respiratory syncytial virus are used to further explore the properties of SGKDE priors when designing a new clinical trial while incorporating historical data. Overall, both studies suggest that the new approach results in improved parameter estimation and power in the current data analysis compared to the considered existing methods.

Estimating the effect of nintedanib on forced vital capacity in children and adolescents with fibrosing interstitial lung disease using a Bayesian dynamic borrowing approach.

The rarity of childhood interstitial lung disease (chILD) makes it challenging to conduct powered trials. In the InPedILD trial, among 39 children and adolescents with fibrosing ILD, there was a numerical benefit of nintedanib versus placebo on change in forced vital capacity (FVC) over 24 weeks (difference in mean change in FVC % predicted of 1.21 [95% confidence interval: -3.40, 5.81]). Nintedanib has shown a consistent effect on FVC across populations of adults with different diagnoses of fibrosing ILD. In a Bayesian dynamic borrowing analysis, prespecified before data unblinding, we incorporated data on the effect of nintedanib in adults and the data from the InPedILD trial to estimate the effect of nintedanib on FVC in children and adolescents with fibrosing ILD. The data from adults were represented as a meta-analytic predictive (MAP) prior distribution with mean 1.69 (95% credible interval: 0.49, 3.08). The adult data were weighted according to expert judgment on their relevance to the efficacy of nintedanib in chILD, obtained in a formal elicitation exercise. Combined data from the MAP prior and InPedILD trial analyzed within the Bayesian framework resulted in a median difference between nintedanib and placebo in change in FVC % predicted at Week 24 of 1.63 (95% credible interval: -0.69, 3.40). The posterior probability for superiority of nintedanib versus placebo was 95.5%, reaching the predefined success criterion of at least 90%. These findings, together with the safety data from the InPedILD trial, support the use of nintedanib in children and adolescents with fibrosing ILDs.

Robust incorporation of historical information with known type I error rate inflation.

Bayesian clinical trials can benefit from available historical information through the specification of informative prior distributions. Concerns are however often raised about the potential for prior-data conflict and the impact of Bayes test decisions on frequentist operating characteristics, with particular attention being assigned to inflation of type I error (TIE) rates. This motivates the development of principled borrowing mechanisms, that strike a balance between frequentist and Bayesian decisions. Ideally, the trust assigned to historical information defines the degree of robustness to prior-data conflict one is willing to sacrifice. However, such relationship is often not directly available when explicitly considering inflation of TIE rates. We build on available literature relating frequentist and Bayesian test decisions, and investigate a rationale for inflation of TIE rate which explicitly and linearly relates the amount of borrowing and the amount of TIE rate inflation in one-arm studies. A novel dynamic borrowing mechanism tailored to hypothesis testing is additionally proposed. We show that, while dynamic borrowing prevents the possibility to obtain a simple closed-form TIE rate computation, an explicit upper bound can still be enforced. Connections with the robust mixture prior approach, particularly in relation to the choice of the mixture weight and robust component, are made. Simulations are performed to show the properties of the approach for normal and binomial outcomes, and an exemplary application is demonstrated in a casestudy.

Enhancing phase I dose-finding trials design through dynamic borrowing information and handling late-onset toxicity

Enhancing phase I dose-finding trials design through dynamic borrowing information and handling late-onset toxicity

A frequentist design for basket trials using adaptive lasso.

A basket trial aims to expedite the drug development process by evaluating a new therapy in multiple populations within the same clinical trial. Each population, referred to as a "basket", can be defined by disease type, biomarkers, or other patient characteristics. The objective of a basket trial is to identify the subset of baskets for which the new therapy shows promise. The conventional approach would be to analyze each of the baskets independently. Alternatively, several Bayesian dynamic borrowing methods have been proposed that share data across baskets when responses appear similar. These methods can achieve higher power than independent testing in exchange for a risk of some inflation in the type 1 error rate. In this paper we propose a frequentist approach to dynamic borrowing for basket trials using adaptive lasso. Through simulation studies we demonstrate adaptive lasso can achieve similar power and type 1 error to the existing Bayesian methods. The proposed approach has the benefit of being easier to implement and faster than existing methods. In addition, the adaptive lasso approach is very flexible: it can be extended to basket trials with any number of treatment arms and any type of endpoint.

Incorporation of healthy volunteers data on receptor occupancy into a phase II proof-of-concept trial using a Bayesian dynamic borrowing design.

Receptor occupancy in targeted tissues measures the proportion of receptors occupied by a drug at equilibrium and is sometimes used as a surrogate of drug efficacy to inform dose selection in clinical trials. We propose to incorporate data on receptor occupancy from a phase I study in healthy volunteers into a phase II proof-of-concept study in patients, with the objective of using all the available evidence to make informed decisions. A minimal physiologically based pharmacokinetic modeling is used to model receptor occupancy in healthy volunteers and to predict it in the patients of a phase II proof-of-concept study, taking into account the variability of the population parameters and the specific differences arising from the pathological condition compared to healthy volunteers. Then, given an estimated relationship between receptor occupancy and the clinical endpoint, an informative prior distribution is derived for the clinical endpoint in both the treatment and control arms of the phase II study. These distributions are incorporated into a Bayesian dynamic borrowing design to supplement concurrent phase II trial data. A simulation study in immuno-inflammation demonstrates that the proposed design increases the power of the study while maintaining a type I error at acceptable levels for realistic values of the clinicalendpoint.

Evaluating hybrid controls methodology in early-phase oncology trials: A simulation study based on the MORPHEUS-UC trial.

Phase Ib/II oncology trials, despite their small sample sizes, aim to provide information for optimal internal company decision-making concerning novel drug development. Hybrid controls (a combination of the current control arm and controls from one or more sources of historical trial data [HTD]) can be used to increase statistical precision. Here we assess combining two sources of Roche HTD to construct a hybrid control in targeted therapy for decision-making via an extensive simulation study. Our simulations are based on the real data of one of the experimental arms and the control arm of the MORPHEUS-UC Phase Ib/II study and two Roche HTD for atezolizumab monotherapy. We consider potential complications such as model misspecification, unmeasured confounding, different sample sizes of current treatment groups, and heterogeneity among the three trials. We evaluate two frequentist methods (with both Cox and Weibull accelerated failure time [AFT] models) and three different commensurate priors in Bayesian dynamic borrowing (with a Weibull AFT model), and modifications within each of those, when estimating the effect of treatment on survival outcomes and measures of effect such as marginal hazard ratios. We assess the performance of these methods in different settings and the potential of generalizations to supplement decisions in early-phase oncology trials. The results show that the proposed joint frequentist methods and noninformative priors within Bayesian dynamic borrowing with no adjustment on covariates are preferred, especially when treatment effects across the three trials are heterogeneous. For generalization of hybrid control methods in such settings, we recommend more simulation studies.

Effectiveness of maternal immunisation with a three-component acellular pertussis vaccine at preventing pertussis in infants in the United States: Post-hoc analysis of a case-control study using Bayesian dynamic borrowing

BackgroundImmunisation during pregnancy with a tetanus toxoid, reduced diphtheria toxoid and acellular pertussis (Tdap) vaccine can protect infants against pertussis between birth and paediatric vaccination. We aimed to estimate the vaccine effectiveness (VE) of third-trimester pregnancy immunisation with the three-component acellular pertussis (Td3ap) vaccine at preventing pertussis in infants <2 months in the United States (US), to support a label update. MethodsWe performed a post-hoc sub-analysis of a case-control study conducted in six US Emerging Infections Program Network states between 2011 and 2014. Our analysis included only cases and controls whose mothers were either vaccinated with Td3ap or did not receive any Tdap vaccine. The association between Td3ap maternal immunisation and pertussis in infants was assessed for US data using a frequentist method with conditional logistic regression. A robustified analysis was conducted using Bayesian dynamic borrowing of non-US data, considering a mixing-weighted prior of 90% for historical non-US VE data, and of 10% for a vague prior. VE was estimated as (1-odds ratio) × 100%. Sensitivity analyses accounting for the impact of each non-US study, different mixing weights and missing/ambiguous data were performed. ResultsWe included 108 cases and 183 controls. Based on US data, the estimated VE of third-trimester maternal immunisation with Td3ap at preventing pertussis in infants <2 months was 78.0% (95% confidence interval: −38.0; 96.5). VE estimated by Bayesian dynamic borrowing of non-US data (with a 90% weight for historical data) was 83.4% (95% credible interval: 55.7; 92.5); sensitivity analyses produced similar VE estimates. ConclusionsEffectiveness of third-trimester pregnancy immunisation with Td3ap at preventing infant pertussis in the US is very likely to be ≥ 50% and is most likely ∼ 80%. Bayesian dynamic borrowing of non-US VE data allowed overcoming the limited power (due to small sample size) of a brand-specific sub-analysis by considering additional evidence.

A Propensity-Score Integrated Approach to Bayesian Dynamic Power Prior Borrowing

Use of historical control data to augment a small internal control arm in a randomized control trial (RCT) can lead to significant improvement of the efficiency of the trial. It introduces the risk of potential bias, since the historical control population is often rather different from the RCT. Power prior approaches have been introduced to discount the historical data to mitigate the impact of the population difference. However, even with a Bayesian dynamic borrowing which can discount the historical data based on the outcome similarity of the two populations, a considerable population difference may still lead to a moderate bias. Hence, a robust adjustment for the population difference using approaches such as the inverse probability weighting or matching, can make the borrowing more efficient and robust. In this article, we propose a novel approach integrating the propensity score for the covariate adjustment and Bayesian dynamic borrowing using power prior. The proposed approach uses Bayesian bootstrap in combination with the empirical Bayes (EB) method using quasi-likelihood for determining the power prior. The performance of our approach is examined by a simulation study. We apply the approach to two Acute Myeloid Leukemia (AML) studies for illustration.

A frequentist approach to dynamic borrowing.

There has been growing interest in leveraging external control data to augment a randomized control group data in clinical trials and enable more informative decision making. In recent years, the quality and availability of real-world data have improved steadily as external controls. However, information borrowing by directly pooling such external controls with randomized controls may lead to biased estimates of the treatment effect. Dynamic borrowing methods under the Bayesian framework have been proposed to better control the false positive error. However, the numerical computation and, especially, parameter tuning, of those Bayesian dynamic borrowing methods remain a challenge in practice. In this paper, we present a frequentist interpretation of a Bayesian commensurate prior borrowing approach and describe intrinsic challenges associated with this method from the perspective of optimization. Motivated by this observation, we propose a new dynamic borrowing approach using adaptive lasso. The treatment effect estimate derived from this method follows a known asymptotic distribution, which can be used to construct confidence intervals and conduct hypothesis tests. The finite sample performance of the method is evaluated through extensive Monte Carlo simulations under different settings. We observed highly competitive performance of adaptive lasso compared to Bayesian approaches. Methods for selecting tuning parameters are also thoroughly discussed based on results from numerical studies and an illustrationexample.

Utilization of treatment effect on a surrogate endpoint for planning a study to evaluate treatment effect on a final endpoint.

To design a phase III study with a final endpoint and calculate the required sample size for the desired probability of success, we need a good estimate of the treatment effect on the endpoint. It is prudent to fully utilize all available information including the historical and phase II information of the treatment as well as external data of the other treatments. It is not uncommon that a phase II study may use a surrogate endpoint as the primary endpoint and has no or limited data for the final endpoint. On the other hand, external information from the other studies for the other treatments on the surrogate and final endpoints may be available to establish a relationship between the treatment effects on the two endpoints. Through this relationship, making full use of the surrogate information may enhance the estimate of the treatment effect on the final endpoint. In this research, we propose a bivariate Bayesian analysis approach to comprehensively deal with the problem. A dynamic borrowing approach is considered to regulate the amount of historical data and surrogate information borrowing based on the level of consistency. A much simpler frequentist method is also discussed. Simulations are conducted to compare the performances of different approaches. An example is used to illustrate the applications of the methods.

Bayesian historical borrowing with longitudinal large-scale assessments

The purpose of this paper is to extend and evaluate methods of Bayesian historical borrowing applied to longitudinal data with a focus on parameter recovery and predictive performance. Bayesian historical borrowing allows researchers to utilize information from previous data sources and to adjust the extent of borrowing based on the similarity of current data to historical data. We examine the utility of three static historical borrowing methods including complete pooling, Bayesian synthesis with aggregated data-dependent priors, traditional power priors, and two dynamic borrowing methods including Bayesian dynamic borrowing and commensurate priors. Using data from two administrations of the United States Early Childhood Longitudinal Study, we evaluate these methods in terms of in-sample simulation statistics, as well as pseudo out-of-sample measures of predictive performance. A case study examining growth in reading competency over time revealed that for one historical cycle, most methods of historical borrowing perform similarly with respect to predictive performance and parameter recovery. Pooling and power priors performed relatively poorly across the conditions in this study, particularly when the current data and the historical data were heterogeneous. Results from a comprehensive simulation study revealed that the advantages of different historical borrowing methods vary across different evaluation criteria. Overall, Bayesian dynamic borrowing and commensurate priors are no worse, and in some cases better, than other methods in terms of parameter recovery and predictive performance, and considering a previous paper by Kaplan et al. (Psychometrika, 10.1007/s11336-022-09869-3, 2022) found clear benefits of Bayesian dynamic borrowing over other methods of historical borrowing in the multilevel context using data from the Program for International Student Assessment (PISA) with five historical cycles, this paper argues that Bayesian dynamic borrowing or commensurate priors is a prudent choice for borrowing information from previous cycles of longitudinal data.

Vaccine clinical trials with dynamic borrowing of historical controls: Two retrospective studies.

Traditional vaccine efficacy trials usually use fixed designs with fairly large sample sizes. Recruiting a large number of subjects requires longer time and higher costs. Furthermore, vaccine developers are more than ever facing the need to accelerate vaccine development to fulfill the public's medical needs. A possible approach to accelerate development is to use the method of dynamic borrowing of historical controls in clinical trials. In this paper, we evaluate the feasibility and the performance of this approach in vaccine development by retrospectively analyzing two real vaccine studies: a relatively small immunological trial (typical early phase study) and a large vaccine efficacy trial (typical Phase 3 study) assessing prophylactic human papillomavirus vaccine. Results are promising, particularly for early development immunological studies, where the adaptive design is feasible, and control of type I error is less relevant.